Gomez, Daniel and Montero de Juan, Francisco Javier
(2008)
*Fuzzy sets in remote sensing classification.*
Soft Computing, 12
.
pp. 243-249.
ISSN 1432-7643

PDF
Restringido a Repository staff only hasta 2020. 178kB |

Official URL: http://www.springerlink.com/content/462841hxl7l71g53/fulltext.pdf

## Abstract

Supervised classification in remote sensing is a very complex problem and involves steps of different nature, including a serious data preprocessing. The final objective can be stated in terms of a classification of isolated pixels between classes, which can be either previously known or not (for example, different land uses), but with no particular shape nither well defined borders. Hence, a fuzzy approach seems natural in order to capture the structure of the image. In this paper we stress that some useful tools for a fuzzy classification can be derived from fuzzy coloring procedures, to be extended in a second stage to the complete non visible spectrum. In fact, the image is considered here as a fuzzy graph defined on the set of pixels, taking advantage of fuzzy numbers in order to summarize information. A fuzzy model is then presented, to be considered as a decision making aid tool. In this way we generalize the classical definition of fuzzy partition due to Ruspini, allowing in addition a first evaluation of the quality of the classification in this way obtained, in terms of three basic indexes (measuring covering, relevance and overlapping of our family of classes).

Item Type: | Article |
---|---|

Uncontrolled Keywords: | Fuzzy classification systems; Remote sensing; Fuzzy graph |

Subjects: | Sciences > Mathematics > Mathematical statistics |

ID Code: | 16297 |

References: | Amo A, Gomez D, Montero J, Biging G (2001) Relevance and redundancy in fuzzy classification systems. Mathware Soft Comput 8:203–216 Amo A, Montero J, Biging G, Cutello V (2004) Fuzzy classification systems. Eur J Oper Res 156:459–507 AmoA, Montero J, Molina E (2001) Representation of consistent recursive rules. Eur J Oper Res 130:29–53 Binahi E, RampiniA(1993) Fuzzy decision-making in the classification of multisource remote-sensing data. Opt Eng 32:1193–1204 BensaidAM, Hall LO, Bezdek JC et al. (1996)Validity-guided (re)clustering with applications to image segmentation. IEEE Trans Fuzzy Systems 4:112–123 Bezdek JC, Harris JD (1978) Fuzzy partitions y Relations, an axiomatic basis for clustering. Fuzzy Sets Systems 1:111–127 Calvo T, Mayor G, Mesiar R (2002) Aggregation operators. Physica-Verlag, Heidelberg Cutello C, Montero J (1975) Hierarchical aggregation of OWA operators: basic measures and related computational problems. Uncertainty, fuzziness and knowledge-based systems 3:17–26 Cutello V, Montero J (1999) Recursive connective rules. Int J Intell Systems 14:3–20 Cogalton RG, Green K (1999) Assessing the accuracy of remote sensed data: principles and practices. Lewis publishers, London, New York Dubois D, Prade H (1983) Ranking fuzzy numbers in the setting of possibility theory. Inf Sci 30:183–224 Fisher PF, Pathirana S (1990) The evaluation of fuzzy membership of land cover classes in the suburban zone. Remote Sens Environ 34:121–132 FoodyGM(1999) The continuum of classification fuzziness in thematic mappings. hotogrammetr Eng Remote Sens 65:443–451 Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Int J Remote Sens 17:1317–1340 Foody GM, Cox DP (1994) Sub-pixel land-cover composition estimation using a linear mixturemodel and fuzzy membership functions. Int J Remote Sens 15:619–631 Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11:773–781 Gómez D, Montero J (2004) A discussion on aggregation operators. Kybernetika 40:107–120 Gómez D, Montero J, Yáñez J, Poidomani C (2007) A graph coloring algorithm approach for image segmentation. Omega 35:173–183 Gómez D, Montero J, Yáñez J (2006) A coloring fuzzy graph approach for image classification. Inf Sci 176:3645–3657 Klement EP, Mesiar R, Pap E (2000) Triangular Norms. Kluwer, Dordrecht Iancu I (1999) Connectives for fuzzy partitions. Fuzzy Sets Systems 101:509–512 Pardalos PM, Mavridou T, Xue J (1998) The Graph Coloring Problem: A Bibliographic Survey. In: Du DZ, Pardalos PM (eds) Handbook of combinatorial optimization, vol 2. Kluwer, Boston, pp 331–395 Ruspini EH (1969) A new approach to clustering. Inf Control 15:22–32 Ruspini EH (1970) Numerical methods for fuzzy clustering. Inf Sci 2, p 319 Thiele H (1996a) A characterization of Ruspini-partitions by similarity relations. In: roceedings of the IPMU’96 conference, pp 389–394 Thiele H (1996b) A characterization of arbitrary Ruspini-partitions by fuzzy similarity relations. In: Proceedings of the IPMU’96 conference, pp 131–134 Wasilakos A, Stathakis D, Wang F (1990) Fuzzy supervised classification of remote-sensing images. Soft Comput 9(5):332–340 Wang F (1990) Fuzzy supervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 28:194–201 Yager RR (1993) Families of OWA operators. Fuzzy Sets Systems 59:125–148 Zadeh LA (1965) Fuzzy sets. Inf Control 8:378–453 |

Deposited On: | 10 Sep 2012 09:45 |

Last Modified: | 07 Feb 2014 09:26 |

Repository Staff Only: item control page